BREAST CANCER PREDICTION¶
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# import libraries
import pandas as pd # for data manupulation or analysis
import numpy as np # for numeric calculation
import matplotlib.pyplot as plt # for data visualization
import seaborn as sns # for data visualization
import dash
import plotly as py
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#Load breast cancer dataset
from sklearn.datasets import load_breast_cancer
cancer_dataset = load_breast_cancer()
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type(cancer_dataset)
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# keys in dataset
cancer_dataset.keys()
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# featurs of each cells in numeric format
cancer_dataset['data']
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these numeric values are extracted values of each cell¶
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# malignant or benign value
cancer_dataset['target']
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The Target stores the Malignant or Benign values¶
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# target value name malignant or benign tumor
cancer_dataset['target_names']
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0 means malignant tumor, 1 mean benign tumor¶
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# description of data
print(cancer_dataset['DESCR'])
Features name of malignant & benign tumor.¶
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print(cancer_dataset['feature_names'])
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# location/path of data file
print(cancer_dataset['filename'])
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# create datafrmae
cancer_df = pd.DataFrame(np.c_[cancer_dataset['data'],cancer_dataset['target']],
columns = np.append(cancer_dataset['feature_names'], ['target']))
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# Head of cancer DataFrame
cancer_df.head(8)
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# Tail of cancer DataFrame
cancer_df.tail(8)
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# Information of cancer Dataframe
cancer_df.info()
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# Numerical distribution of data
cancer_df.describe()
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Data Visualization¶
Pair plot of breast cancer data¶
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# Paiplot of cancer dataframe
sns.pairplot(cancer_df, hue = 'target')
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# pair plot of sample feature
sns.pairplot(cancer_df, hue = 'target', vars = ['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'mean smoothness'] )
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# Count the target class
sns.countplot(cancer_df['target'])
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# counter plot of feature mean radius
plt.figure(figsize = (20,8))
sns.countplot(cancer_df['mean radius'])
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Heatmap¶
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# heatmap of DataFrame
plt.figure(figsize=(16,9))
sns.heatmap(cancer_df)
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Heatmap of a correlation matrix¶
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# Heatmap of Correlation matrix of breast cancer DataFrame
plt.figure(figsize=(20,20))
sns.heatmap(cancer_df.corr(), annot = True, cmap ='coolwarm', linewidths=2)
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Correlation barplot¶
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# create second DataFrame by droping target
cancer_df2 = cancer_df.drop(['target'], axis = 1)
print("The shape of 'cancer_df2' is : ", cancer_df2.shape)
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# visualize correlation barplot
plt.figure(figsize = (16,5))
ax = sns.barplot(cancer_df2.corrwith(cancer_df.target).index, cancer_df2.corrwith(cancer_df.target))
ax.tick_params(labelrotation = 90)
Data Preprocessing¶
Split DataFrame in train and test¶
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# input variable
X = cancer_df.drop(['target'], axis = 1)
X.head(8)
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# output variable
y = cancer_df['target']
y.head(8)
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# split dataset into train and test
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state= 5)
Feature Scaling¶
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# Feature scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train_sc = sc.fit_transform(X_train)
X_test_sc = sc.transform(X_test)
Model Building¶
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from sklearn.metrics import confusion_matrix, classification_report, accuracy_score
from xgboost import XGBClassifier # XGBoost Classifier
xgb_classifier = XGBClassifier()
xgb_classifier.fit(X_train, y_train)
y_pred_xgb = xgb_classifier.predict(X_test)
accuracy_score(y_test, y_pred_xgb)
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# Train with Standard scaled Data
xgb_classifier2 = XGBClassifier()
xgb_classifier2.fit(X_train_sc, y_train)
y_pred_xgb_sc = xgb_classifier2.predict(X_test_sc)
accuracy_score(y_test, y_pred_xgb_sc)
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# XGBoost classifier most required parameters
params={
"learning_rate" : [0.05, 0.10, 0.15, 0.20, 0.25, 0.30 ] ,
"max_depth" : [ 3, 4, 5, 6, 8, 10, 12, 15],
"min_child_weight" : [ 1, 3, 5, 7 ],
"gamma" : [ 0.0, 0.1, 0.2 , 0.3, 0.4 ],
"colsample_bytree" : [ 0.3, 0.4, 0.5 , 0.7 ]
}
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# Randomized Search
from sklearn.model_selection import RandomizedSearchCV
random_search = RandomizedSearchCV(xgb_classifier, param_distributions=params, scoring= 'roc_auc', n_jobs= -1, verbose= 3)
random_search.fit(X_train, y_train)
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random_search.best_params_
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random_search.best_estimator_
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# training XGBoost classifier with best parameters
xgb_classifier_pt = XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=0.4, gamma=0.2,
learning_rate=0.1, max_delta_step=0, max_depth=15,
min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,
nthread=None, objective='binary:logistic', random_state=0,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
silent=None, subsample=1, verbosity=1)
xgb_classifier_pt.fit(X_train, y_train)
y_pred_xgb_pt = xgb_classifier_pt.predict(X_test)
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accuracy_score(y_test, y_pred_xgb_pt)
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Confusion Matrix¶
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cm = confusion_matrix(y_test, y_pred_xgb_pt)
plt.title('Heatmap of Confusion Matrix', fontsize = 15)
sns.heatmap(cm, annot = True)
plt.show()
Classification Report of Model¶
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print(classification_report(y_test, y_pred_xgb_pt))
Cross-validation of the ML model¶
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from sklearn.model_selection import cross_val_score
cross_validation = cross_val_score(estimator = xgb_classifier_pt, X = X_train_sc,y = y_train, cv = 10)
print("Cross validation accuracy of XGBoost model = ", cross_validation)
print("\nCross validation mean accuracy of XGBoost model = ", cross_validation.mean())
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## Pickle
import pickle
# save model
pickle.dump(xgb_classifier_pt, open('breast_cancer_detector.pickle', 'wb'))
# load model
breast_cancer_detector_model = pickle.load(open('breast_cancer_detector.pickle', 'rb'))
# predict the output
y_pred = breast_cancer_detector_model.predict(X_test)
# confusion matrix
print('Confusion matrix of XGBoost model: \n',confusion_matrix(y_test, y_pred),'\n')
# show the accuracy
print('Accuracy of XGBoost model = ',accuracy_score(y_test, y_pred))